New predictive equations for estimating resting energy expenditure in subjects with normal weight and overweight

•Indirect calorimetry is considered the reference method for the measurement of resting energy expenditure (REE).•Predictive equations are commonly used as an alternative method for estimating REE.•New REE equations with bioimpedance analysis (BIA) variables for subjects with normal weight and overw...

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Bibliographic Details
Published in:Nutrition (Burbank, Los Angeles County, Calif.) Los Angeles County, Calif.), 2021-04, Vol.84, p.111105-111105, Article 111105
Main Authors: Marra, Maurizio, Sammarco, Rosa, Cioffi, Iolanda, Morlino, Delia, Di Vincenzo, Olivia, Speranza, Enza, Pasanisi, Fabrizio
Format: Article
Language:English
Subjects:
Accuracy
Age
Bias
Bioimpedance analysis
Body composition
Body mass index
Body size
Body weight
Calibration
Calorimetry
Carbon dioxide
Clinical medicine
Energy
Energy expenditure
Estimation
Formulas (mathematics)
Indirect calorimetry
Mathematical models
Men
Normal weight
Overweight
Phase angle
Predictive equations
Resting energy expenditure
Sexes
Variables
Womens health
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Summary:•Indirect calorimetry is considered the reference method for the measurement of resting energy expenditure (REE).•Predictive equations are commonly used as an alternative method for estimating REE.•New REE equations with bioimpedance analysis (BIA) variables for subjects with normal weight and overweight.•The equation with raw BIA variables showed the highest accuracy at individual level. The aim of this study was to develop and validate new predictive equations for estimating resting energy expenditure (REE) in subjects with normal weight and overweight, considering anthropometric parameters as well as raw variables from bioimpedance analysis (BIA). Adult participants with normal weight and overweight were recruited and randomly split into calibration and validation groups. Indirect calorimetry (IC) and BIA were performed in all subjects. New predictive equations were developed using the following models: model 1 with age, weight, stature, and body mass index (BMI) as predictors; and model 2: model 1 + raw BIA variables (bioimpedance index and phase angle). The accuracy of the new equations at both the group (bias) and individual (within ±10%) levels was tested in the validation group. Three published predictive equations were also compared, with the REE values measured by IC. A total of 2483 adults were included for developing and validating the new equations. All selected formulas, including the new ones, showed a bias of
ISSN:0899-9007
1873-1244
DOI:10.1016/j.nut.2020.111105